Optimal Design of a Solar-Driven Heat Engine Based on Thermal and Ecological Criteria
Publication: Journal of Energy Engineering
Volume 141, Issue 3
Abstract
Optimal thermal and ecological performance tests of a solar-driven heat engine system were conducted in various optimization scenarios. For this, thermal and ecological models were developed for the solar-driven heat engine, and three objective functions (including thermal efficiency, output power, and ecological function) were obtained for the proposed system. In the present investigation, thermodynamic analysis and an evolutionary algorithm (EA) were employed to optimize the dimensionless ecological function, thermal efficiency, and dimensionless power of a solar-driven engine system. Four scenarios were conducted for optimization of the solar heat engine. In the first three, a traditional single-objective optimization was employed separately with each objective function, regardless of other objectives. In the fourth scenario, efficiency and power objectives were optimized simultaneously using a nondominated sorting genetic algorithm (GA) called the nondominated sorting genetic algorithm (NSGA-II). As in multiobjective optimization, a set of optimal solutions called the Pareto optimal frontier was obtained instead of a single final optimal solution obtained in traditional single-objective optimization. Therefore, a process of decision making was employed for selecting a final optimal solution. Three decision-making procedures were applied to find optimized solutions from the Pareto optimal solutions in the objectives’ space. The results obtained from four optimization scenarios were compared and discussed using a deviation index introduced in this paper. It was shown that the optimal results obtained in single-objective optimization with an ecological objective are very close to the corresponding results obtained in the multiobjective optimization, in that the power and thermal efficiency are optimized simultaneously. Finally, an error analysis was performed, indicating that the error in evaluating the final solution was less than 0.3%.
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Published In
Journal of Energy Engineering
Volume 141 • Issue 3 • September 2015
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© 2014 American Society of Civil Engineers.
History
Received: Oct 10, 2013
Accepted: Jan 28, 2014
Published online: Mar 10, 2014
Discussion open until: Aug 10, 2014
Published in print: Sep 1, 2015
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